Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

/pdf/data-mining-in-clinical-big-data-the-frequently-used-1dzr75xywz.pdf

Data mining in clinical big data: the frequently used databases, steps, and methodological models.

Integration on ℋ_{}×ℋ

Aim Eating disorders have increasingly become a public health concern globally. This study aimed to reveal the burden of eating disorders at the global, regional and national levels using the Global Burden of Disease (GBD) Study 2017 data. Methods
 We extracted the age-standardised rates (ASRs) of prevalence and disability-adjusted life years (DALYs) and their 95% uncertainty intervals (UIs) of eating disorders, including anorexia nervosa and bulimia nervosa, between 1990 and 2017 from the GBD 2017 data. The estimated annual percentage changes (EAPCs) were calculated to quantify the secular trends of the burden of eating disorders. Results
 The ASRs of prevalence and the DALYs of eating disorders continuously increased worldwide from 1990 to 2017 by an average of 0.65 (95% UI: 0.59–0.71) and 0.66 (95% UI: 0.60–0.72), respectively. The burden of eating disorders was higher in females than in males, but the increment in ASRs was greater in males than in females over time. In 2017, the highest burden of eating disorders was observed in the high sociodemographic index (SDI) regions, especially Australasia (ASR of prevalence = 807.13, 95% UI: 664.20–982.30; ASR of DALYs = 170.74, 95% UI: 113.43–244.14, per 100 000 population), Western Europe and high-income North America. However, the most significant increment of the burden of eating disorders was observed in East Asia (EAPC for prevalence = 2.23, 95% UI: 2.14–2.32; EAPC for DALYs = 2.22, 95% UI: 2.13–2.31), followed by South Asia. An increasing trend in the burden of eating disorders at the national level was observed among most countries or territories. The countries with the top three highest increasing trends were Equatorial Guinea, Bosnia and Herzegovina and China. Positive associations were found between the burden estimates and the SDI levels in almost all geographic regions during the observed 28-year period. We also found that the human development indexes in 2017 were positively correlated with the EAPCs of the ASRs of prevalence (ρ = 0.222, P = 0.002) and DALYs (ρ = 0.208, P = 0.003). Conclusion
 The highest burden of eating disorders remains in the high-income western countries, but an increasing trend was observed globally and in all SDI-quintiles, especially in Asian regions that were highly populous. These results could help governments worldwide formulate suitable medical and health policies for the prevention and early intervention of eating disorders.

/pdf/trends-in-the-prevalence-and-disability-adjusted-life-years-3smm0ziwqf.pdf

Trends in the prevalence and disability-adjusted life years of eating disorders from 1990 to 2017: results from the Global Burden of Disease Study 2017

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Homology modeling in the time of collective and artificial intelligence

Background Colorectal cancer defined as cancer of the colon or rectum, is the third most frequently diagnosed cancer in men and the second in women, and, according to the World Health Organization database GLOBOCAN, it accounts for nearly 1.4 million new cases annually worldwide. The occurrence of colorectal cancer is associated with nonmodifiable risk factors, including age and hereditary factors, as well as with modifiable environmental and lifestyle factors. Methods The study included 800 patients, 400 diagnosed with colorectal cancer and 400 within the control group, who gave their written informed consent to participate in the study. Patients with cancer other than colorectal cancer were randomly selected for control group I, and patients with no cancer diagnosis were selected for control group II. The method used was a case-control study – an observational and analytical study with a control group, conducted among patients of the Clinical Oncology Centre and the Provincial Hospital in the years 2019–2020. The study comparing the exposure was carried out in a group of people who developed the endpoint, that is colorectal cancer, with the exposure in a well-matched group of controls who did not reach the endpoint. Assessment of activity and BMI was used according to WHO recommendations, as well as the expert system. The data were tested for the distribution and the homogeneity of variance was validated before applying the parameter tests. Comparison of quantitative variables between groups was performed using ANOVA. Results The mean age of the patients was 64.53 ± 8.86 years, of the control group I – 59.64 ± 9.33 and the control group II – 57.5 (7.83). There was a strong positive association between the incidence of ulcerative colitis and the risk of colorectal cancer (P < .01). Among obese subjects, the risk of developing colorectal cancer was 1.27 (95% CI, 1.06–1.53) compared with nonobese subjects. A strong positive relationship was found between low physical activity converted to metabolic equivalent of MET effort per week and the risk of colorectal cancer (P < .001). The relative risk for current smokers was 2.17 (95% CI 1.79–2.66). There was an association between higher fat consumption and higher red meat consumption and the risk of developing colorectal cancer (P < .01). Conclusions Obesity, low physical activity, active and passive smoking and high salt and red meat consumption have been associated with an increased risk of colorectal cancer. These findings provide further evidence of the importance of maintaining a healthy lifestyle.

https://journals.sagepub.com/doi/pdf/10.1177/10732748211056692

Title: Risk Factors for the Diagnosis of Colorectal Cancer

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/jebm.12373

Brief introduction of medical database and data mining technology in big data era

Background: Patients with metabolic syndrome (MetS) have a higher risk of developing cardiovascular diseases (CVD). However, controversy exists about the impact of MetS on the prognosis of patients with CVD. Methods: Pubmed, Cochrane library, and EMBASE databases were searched. Cohort Studies and randomized controlled trials post hoc analyses that evaluated the impact of MetS on prognosis in patients (≥18 years) with CVD were included. Relative risk (RR), hazard rate (HR) and 95% confidence intervals (CIs) were calculated for each individual study by random-effect model. Subgroup analysis and meta-regression analysis was performed to explore the heterogeneity. Results: 55 studies with 16,2450 patients were included. Compared to patients without MetS, the MetS was associated with higher all-cause death [RR, 1.220, 95% CI (1.103 to 1.349), P, 0.000], CV death [RR, 1.360, 95% CI (1.152 to 1.606), P, 0.000], Myocardial Infarction [RR, 1.460, 95% CI (1.242 to 1.716), P, 0.000], stroke [RR, 1.435, 95% CI (1.131 to 1.820), P, 0.000]. Lower high-density lipoproteins (40/50) significantly increased the risk of all-cause death and CV death. Elevated fasting plasma glucose (FPG) (>100 mg/dl) was associated with an increased risk of all-cause death, while a higher body mass index (BMI>25 kg/m2) was related to a reduced risk of all-cause death. Conclusions: MetS increased the risk of cardiovascular-related adverse events among patients with CVD. For MetS components, there was an increased risk in people with low HDL-C and FPG>100 mg/dl. Positive measures should be implemented timely for patients with CVD after the diagnosis of MetS, strengthen the prevention and treatment of hyperglycemia and hyperlipidemia.

/pdf/impact-of-metabolic-syndrome-and-it-s-components-on-b8m1zrb9o0.pdf

Impact of Metabolic Syndrome and It's Components on Prognosis in Patients With Cardiovascular Diseases: A Meta-Analysis.

The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma. Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram. We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.

/pdf/a-convenient-clinical-nomogram-for-predicting-the-cancer-2wn12dnowz.pdf

Li Li

Papers

Brief introduction of medical database and data mining technology in big data era

Data mining in clinical big data: the frequently used databases, steps, and methodological models.

Impact of Metabolic Syndrome and It's Components on Prognosis in Patients With Cardiovascular Diseases: A Meta-Analysis.

A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma